Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

Mohammadi, Hamid Reza; Akhavan, Ali

doi:10.1155/2014/148204

Cited by 42 publications

(24 citation statements)

References 14 publications

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“…Different from most of the existent evolutionary algorithms, the GSA presents a better performance in multimodal problems, avoiding critical flaws such as the premature convergence to sub-optimal solutions [13,14]. In the GSA, candidate solutions emulate masses which attract each other through operators that mimic the gravitational force.…”

Section: Gravitational Search Algorithmmentioning

confidence: 99%

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Induction Motor Parameter Identification Using a Gravitational Search Algorithm

2016

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Abstract:The efficient use of electrical energy is a topic that has attracted attention for its environmental consequences. On the other hand, induction motors represent the main component in most industries. They consume the highest energy percentages in industrial facilities. This energy consumption depends on the operation conditions of the induction motor imposed by its internal parameters. Since the internal parameters of an induction motor are not directly measurable, an identification process must be conducted to obtain them. In the identification process, the parameter estimation is transformed into a multidimensional optimization problem where the internal parameters of the induction motor are considered as decision variables. Under this approach, the complexity of the optimization problem tends to produce multimodal error surfaces for which their cost functions are significantly difficult to minimize. Several algorithms based on evolutionary computation principles have been successfully applied to identify the optimal parameters of induction motors. However, most of them maintain an important limitation: They frequently obtain sub-optimal solutions as a result of an improper equilibrium between exploitation and exploration in their search strategies. This paper presents an algorithm for the optimal parameter identification of induction motors. To determine the parameters, the proposed method uses a recent evolutionary method called the gravitational search algorithm (GSA). Different from most of the existent evolutionary algorithms, the GSA presents a better performance in multimodal problems, avoiding critical flaws such as the premature convergence to sub-optimal solutions. Numerical simulations have been conducted on several models to show the effectiveness of the proposed scheme.

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Section: Gravitational Search Algorithmmentioning

confidence: 99%

“…In general, the parameters p f l and P rot are calculated through two experimental tests known as No-load-test and Blocked-rotor-test [23,24]. However, in order to maintain compatibility with similar works reported in the literature, they were obtained from references [11][12][13].…”

Section: Exact Circuit Modelmentioning

confidence: 99%

Induction Motor Parameter Identification Using a Gravitational Search Algorithm

2016

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“…In electrical engineering, the population-based algorithms may be applied to solving of numerous and very diverse problems-e.g., optimization of energy-storage devices in a system containing wind power stations [24], placing of capacitor banks in distribution systems [25], optimization of charging infrastructure in distribution systems [27], and estimation of three-phase asynchronous motor parameters [28].…”

Section: Application Of Swarm Intelligence Algorithmsmentioning

confidence: 99%

Application of Ant-Colony Algorithm to the Issue of Improving Rectified Voltage Parameters in Electric Tram Traction

2018

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In this paper, the problem related to transformation of ac voltage into DC voltage used in tram supply is considered. A variable component is always present in rectified voltage. Pulsation of rectified voltage is influenced by different factors. In a 12pulse system, where two secondary transformer windings are used (one delta-connected and the other star-connected), an additional factor increasing the pulsation is the unbalance of the output voltages at these windings. Tap changer may be used and its setting is optimized here by applying the ant-colony algorithm. Different supply voltage variants have been considered. It is demonstrated that pulsation may be reduced by even 25%.

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“…The main problem to be solved is how to find-quickly and efficiently-the source of food. An extensive list of other algorithms inspired by animals (including swarm) intelligence may be found, e.g., in [6].Electrical engineering has started to adopt population-based algorithms, and they have been applied to solve diverse problems such as, e.g., the wind power station energy storage problem [7], the location of capacitor banks in the distribution system [8], the optimized placing of charging stations in the distribution system [9], the optimization of the voltage profile in the distribution system with distributed electricity sources [10], the assessment of 3-phase induction motor parameters [11], some railway transport issues [12], and current balancing in the railway system [13].We will discuss two technical problems in this paper; to both problems we have applied the ant colony algorithm (ACO) as a solution. One problem is improving the "quality" of the output voltage of the rectifier transformer used in supply of DC tram traction lines; the other problem is equalizing the distribution of loads between secondary windings and rectifiers of the rectifier-transformer set.…”

mentioning

confidence: 99%

“…Electrical engineering has started to adopt population-based algorithms, and they have been applied to solve diverse problems such as, e.g., the wind power station energy storage problem [7], the location of capacitor banks in the distribution system [8], the optimized placing of charging stations in the distribution system [9], the optimization of the voltage profile in the distribution system with distributed electricity sources [10], the assessment of 3-phase induction motor parameters [11], some railway transport issues [12], and current balancing in the railway system [13].…”

mentioning

confidence: 99%

The Application of Ant Colony Algorithms to Improving the Operation of Traction Rectifier Transformers

2019

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In this paper, we discuss a technical issue occurring in electric traction. Tram traction may use DC voltage; this is obtained by rectifying AC voltage supplied by the power grid. In the simplest design-one which is commonly used-only diode uncontrolled rectifiers are used. The rectified voltage is not smooth; it always contains a pulsating (AC) component. The amount of pulsation varies. It depends, among other factors, on the design of the transformer-rectifier set. In the 12-pulse system, we use a three-winding transformer, consisting of one primary winding and two secondary windings: one is delta-connected and the other is star-connected. The unbalance of secondary windings is an extra factor increasing the pulsation of DC voltage. To equalize secondary side voltages, a tap changer may be used. The setting of the tap changer is the question resolved in this paper; it is optimized by application of the ACO (ant colony optimization algorithm). We have analyzed different supply voltage variants, and in particular, distorted voltage containing 5th and 7th harmonics. The results of ant colony optimization application are described in this paper. The General Issue: Applying the Swarm Intelligence Algorithm to Optimization ProblemsEngineering nowadays often requires the numerical optimization of many and diverse problems. New optimization algorithms are invented all the time; among them, we may distinguish different types of metaheuristic algorithms. The heuristic approach may be termed practical; it may not be optimal in the mathematical sense of the word, but it is quite often sufficient enough from engineering viewpoint. Usually, its main advantage lies in its speed-a satisfactory solution may be found more quickly than in cases when more elaborate numerical algorithms are used.Among the metaheuristic algorithms, we find lots of procedures based on swarm intelligence. They are inspired by the behaviour of a large population of individuals belonging to one species. The performance of such a group seems to be co-ordinated, even though we cannot identify a leader, i.e., some sort of central control. Every individual in a swarm sends information and receives information from other members of the group, in a way that is peculiar to a given species. It must be stressed that information on the optimum way of acting (e.g., the shortest way to food source) is also exchanged. The entire swarm (system) progresses dynamically. When some individuals find the most effective course of action, the others (receivers of this information) concentrate in the regions of search areas that have proven to be most successful from the viewpoint of the desired goal (e.g., the source of food). Thus, the solution to the problem is attained [1]. Apart from searching for food, other behaviour patterns such as communication, task allotment, decision making, the localization of HQ, choice of partners, mating, and propagation have been the source of inspiration for population algorithms.Ant colony optimization [2][3][4][5] is one such algorithm. T...

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Parameter Estimation of Three-Phase Induction Motor Using Hybrid of Genetic Algorithm and Particle Swarm Optimization

Cited by 42 publications

References 14 publications

Induction Motor Parameter Identification Using a Gravitational Search Algorithm

Induction Motor Parameter Identification Using a Gravitational Search Algorithm

Application of Ant-Colony Algorithm to the Issue of Improving Rectified Voltage Parameters in Electric Tram Traction

The Application of Ant Colony Algorithms to Improving the Operation of Traction Rectifier Transformers

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